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LiMCA offers a tool for co-profiling 3D genome structure and gene expression at the single-cell level, enabling researchers to elucidate the olfactory receptor gene selection process.
After having been largely neglected for more than a century, the tiny marine animal Trichoplax adhaerens is enjoying a revival and is now poised to become a mainstream model organism for the study of epithelial evolution, diversification and specialization.
A pretrained foundation model (UniFMIR) enables versatile and generalizable performance across diverse fluorescence microscopy image reconstruction tasks.
Several research groups are making it easier for other neuroscientists to analyze large datasets by providing tools that can be accessed and used from anywhere in the world.
brainlife.io is a one-stop cloud platform for data management, visualization and analysis in human neuroscience. It is web-based and provides access to a variety of tools in a reproducible and reliable manner.
The CPJUMP1 Resource comprises Cell Painting images and profiles of 75 million cells treated with hundreds of chemical and genetic perturbations. The dataset enables exploration of their relationships and lays the foundation for the development of advanced methods to match perturbations.
scGHOST offers a computational tool to annotate single-cell subcompartments from scHi-C or imaging data through graph representation learning with constrained random walk sampling.
Kilosort4 is a spike-sorting algorithm with improved performance compared to previous versions, owing to the use of a graph-based clustering approach. The tool extracts the activity of individual neurons from electrophysiological recordings acquired with, for example, Neuropixels electrodes.
The combination of light sheet illumination and reversibly switchable fluorophores enables improved structured illumination microscopy for fast, low-background super-resolution imaging in cells and spheroids.
Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.
RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.
Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.